Insurance Cost Prediction With Linear regression method
Witri Oktafiani Sudjadi
6/4/2023
This project discusses the regression model of an insurance company’s data to predict the insurance premium that must be paid by a policyholder.
Dataset: https://www.kaggle.com/datasets/noordeen/insurance-premium-prediction
Library
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## RecallDataset
# import dataset
insurance <- read.csv("D:/file R/insurance.csv")
head(insurance)## age sex bmi children smoker region expenses
## 1 19 female 27.9 0 yes southwest 16884.92
## 2 18 male 33.8 1 no southeast 1725.55
## 3 28 male 33.0 3 no southeast 4449.46
## 4 33 male 22.7 0 no northwest 21984.47
## 5 32 male 28.9 0 no northwest 3866.86
## 6 31 female 25.7 0 no southeast 3756.62- Age: Age of primary policyholder.
- Sex: Sex of the policy policyholder.
- BMI: Body Mass Index of policyholder
- Smoker: Whether the policyholder is a smoker or a non-smoker.
- Children: Number of children/dependents covered in the policy.
- Region: Residential areas of the policy holder (in the US) - North East, South East, South West, North West.
- Expenses: Yearly medical expenses billed by the medical insurance provider ($).
Exploratory Data Analysis
Check Data Type
str(insurance)## 'data.frame': 1338 obs. of 7 variables:
## $ age : int 19 18 28 33 32 31 46 37 37 60 ...
## $ sex : chr "female" "male" "male" "male" ...
## $ bmi : num 27.9 33.8 33 22.7 28.9 25.7 33.4 27.7 29.8 25.8 ...
## $ children: int 0 1 3 0 0 0 1 3 2 0 ...
## $ smoker : chr "yes" "no" "no" "no" ...
## $ region : chr "southwest" "southeast" "southeast" "northwest" ...
## $ expenses: num 16885 1726 4449 21984 3867 ...There are data types that do not match, namely in the variables sex, smoker, and region.
Change Data Type
insurance[ , c("sex", "smoker", "region")] <-
lapply(insurance[ , c("sex", "smoker", "region")], as.factor)
str(insurance)## 'data.frame': 1338 obs. of 7 variables:
## $ age : int 19 18 28 33 32 31 46 37 37 60 ...
## $ sex : Factor w/ 2 levels "female","male": 1 2 2 2 2 1 1 1 2 1 ...
## $ bmi : num 27.9 33.8 33 22.7 28.9 25.7 33.4 27.7 29.8 25.8 ...
## $ children: int 0 1 3 0 0 0 1 3 2 0 ...
## $ smoker : Factor w/ 2 levels "no","yes": 2 1 1 1 1 1 1 1 1 1 ...
## $ region : Factor w/ 4 levels "northeast","northwest",..: 4 3 3 2 2 3 3 2 1 2 ...
## $ expenses: num 16885 1726 4449 21984 3867 ...Summary Statistics
summary(insurance)## age sex bmi children smoker
## Min. :18.00 female:662 Min. :16.00 Min. :0.000 no :1064
## 1st Qu.:27.00 male :676 1st Qu.:26.30 1st Qu.:0.000 yes: 274
## Median :39.00 Median :30.40 Median :1.000
## Mean :39.21 Mean :30.67 Mean :1.095
## 3rd Qu.:51.00 3rd Qu.:34.70 3rd Qu.:2.000
## Max. :64.00 Max. :53.10 Max. :5.000
## region expenses
## northeast:324 Min. : 1122
## northwest:325 1st Qu.: 4740
## southeast:364 Median : 9382
## southwest:325 Mean :13270
## 3rd Qu.:16640
## Max. :63770- Age of the policyholder are between 18 and 64
- There were 662 female policyholders and 676 male policyholders
- BMI of the policyholder are between 15.96 and 53.13
- Number of children or dependents of the principal range between 0 to 5
- There were 1064 policyholders who did not smoke and 274 who did. The number of policyholders who do not smoke is almost 4 times the number of policyholders who smoke
- The average medical cost is 13,270 and maximum claim value is 63,770
Check Missing Value
colSums(is.na(insurance))## age sex bmi children smoker region expenses
## 0 0 0 0 0 0 0no missing value detected
Correlation
cormat <- cor(insurance[c("age","bmi", "expenses")])
melted_data <- melt(cormat)## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUEhead(melted_data)## X1 X2 value
## 1 age age 1.0000000
## 2 bmi age 0.1093410
## 3 expenses age 0.2990082
## 4 age bmi 0.1093410
## 5 bmi bmi 1.0000000
## 6 expenses bmi 0.1985763Plot Correlation
g <- ggplot(data = melted_data, aes(x=X1, y=X2, fill=value)) + geom_tile()
g <- g + labs(x="", y="") + labs(title="CORRELATION HEATMAP")
g <- g + geom_text(aes(X1,X2, label=round(value,2)), color="white", size=5)
g
Age and BMI variables do not have a strong correlation with the expenses variable
Visualization
Distribution Variable Age
insurance %>%
ggplot(aes(age)) +
geom_histogram(color = "red", fill = "red",alpha = .5, bins = 25) +
scale_x_continuous(breaks = seq(18,64,5)) +
theme(axis.text.x = element_text(size = 10)) +
labs(title="Distribution Variable Age", x="")
Age of the policyholder are between 18 and 64
Distribution Variable Charges
library(ggplot2)
library(dplyr)
insurance %>%
ggplot(aes(expenses)) +
geom_histogram(color = "red", fill = "red",alpha = .5, bins = 25) +
scale_x_continuous(breaks = seq(0,64000,10000)) +
theme(axis.text.x = element_text(size = 10)) +
labs(title="Distribution Variable Expenses", x="")
The average medical cost is 13,270 and maximum claim value is 63,770
Distribution Variable Sex
insurance %>%
ggplot(aes(x=sex, y = prop.table(stat(count)),
fill = sex,
label = scales::percent(prop.table(stat(count))))) +
geom_bar(show.legend=F) +
geom_text(stat = 'count',
position = position_dodge(.9),
vjust = -0.5,
size = 3) +
scale_y_continuous(labels = scales::percent) +
theme(axis.text.x = element_text(size = 10)) +
labs(title="Distribution Variable Sex", x="", y="")## Warning: `stat(count)` was deprecated in ggplot2 3.4.0.
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## generated.
There were 662 female policyholders (49,5%) and 676 male policyholders (50,5%)
Scatter Between Age and Expenses
gscat <- ggplot(insurance, aes(age, expenses, color=sex)) + geom_point()
gscat <- gscat + labs(title = "Age vs Expenses plot")
gscat <- gscat + geom_smooth()
gscat## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Scatter Between BMI and Expenses
gscat2 <- ggplot(insurance, aes(bmi, expenses, color=sex)) + geom_point()
gscat2 <- gscat2 + labs(x="BMI", y="Charges", title="BMI vs Expenses")
gscat2
Building Model
Splitting the data set
Split data into 80% training data and 20% testing data
RNGkind(sample.kind = "Rounding")## Warning in RNGkind(sample.kind = "Rounding"): non-uniform 'Rounding' sampler
## usedset.seed(100)
index_ins <- sample(x = nrow(insurance) , size = nrow(insurance)*0.8)
data_train <- insurance[index_ins , ]
data_test <- insurance[-index_ins, ]
cat(" Size of data train:",dim(data_train),"\n","Size of data test :",dim(data_test))## Size of data train: 1070 7
## Size of data test : 268 7Model with ALL VARIABLES
model_all <- lm(formula = expenses ~ . , data = data_train)
summary(model_all)##
## Call:
## lm(formula = expenses ~ ., data = data_train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10564 -2842 -976 1363 30402
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -12157.96 1103.69 -11.016 < 2e-16 ***
## age 254.36 13.19 19.282 < 2e-16 ***
## sexmale -65.82 368.74 -0.179 0.85836
## bmi 345.97 31.77 10.891 < 2e-16 ***
## children 463.26 151.97 3.048 0.00236 **
## smokeryes 23452.86 457.46 51.268 < 2e-16 ***
## regionnorthwest -155.81 533.18 -0.292 0.77018
## regionsoutheast -1001.55 533.88 -1.876 0.06093 .
## regionsouthwest -1187.37 530.59 -2.238 0.02544 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5998 on 1061 degrees of freedom
## Multiple R-squared: 0.7476, Adjusted R-squared: 0.7457
## F-statistic: 392.8 on 8 and 1061 DF, p-value: < 2.2e-16From the first model, it can be seen that the most significant variables are age, BMI, and smoker. This can be seen from the adjusted R-squared value of 0.7457. This means that based on the predictor variables included in the model, it is able to explain the cost variable by 74.57%, the rest is explained by other variables that are not included in the model.
Feature Selection (backward)
Feature selection is the stage in selecting the variables to be used in the model. This process will evaluate the stepwise model using the AIC (Akaike Information Criterion) value. AIC shows a lot of missing information or information loss on the model.
model_backward <- step(object = model_all, direction = "backward", trace = 0)
summary(model_backward)##
## Call:
## lm(formula = expenses ~ age + bmi + children + smoker + region,
## data = data_train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10596 -2835 -977 1375 30373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -12180.42 1095.99 -11.114 < 2e-16 ***
## age 254.37 13.19 19.292 < 2e-16 ***
## bmi 345.68 31.71 10.901 < 2e-16 ***
## children 462.28 151.80 3.045 0.00238 **
## smokeryes 23447.04 456.08 51.410 < 2e-16 ***
## regionnorthwest -153.79 532.81 -0.289 0.77292
## regionsoutheast -1002.27 533.62 -1.878 0.06062 .
## regionsouthwest -1187.63 530.35 -2.239 0.02534 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5995 on 1062 degrees of freedom
## Multiple R-squared: 0.7476, Adjusted R-squared: 0.7459
## F-statistic: 449.3 on 7 and 1062 DF, p-value: < 2.2e-16- The first model with complete variables has an Adjusted R-Squared value of 0.7457, which means it can explain 74.57% of the variance in the target variable (insurance cost)
- The feature selection model has an adjusted R-squared of 0.7459, which means it can explain 74.59% of the variance in the target variable (insurance cost).
The difference is not too much compared to the first model. This suggests that there is no harm in removing variables that do not have significant coefficient values.
Model Evaluation
Check model evaluation to see the best model performance
library(performance)
compare_performance(model_all, model_backward)## # Comparison of Model Performance Indices
##
## Name | Model | AIC (weights) | AICc (weights) | BIC (weights) | R2 | R2 (adj.) | RMSE | Sigma
## ----------------------------------------------------------------------------------------------------------------------
## model_all | lm | 21663.8 (0.272) | 21664.0 (0.268) | 21713.6 (0.030) | 0.748 | 0.746 | 5972.942 | 5998.222
## model_backward | lm | 21661.8 (0.728) | 21662.0 (0.732) | 21706.6 (0.970) | 0.748 | 0.746 | 5973.032 | 5995.487Prediction
Prediction of train data set
pred_train <- predict(model_backward, newdata = data_train)
pred_train <- data_train$prediction
MAPE(model_backward$fitted.values, data_train$expenses)*100## [1] 42.98898Prediction of test data set
pred_test <- predict(model_backward, newdata = data_test)
pred_test <- data_test$prediction
MAPE(model_all$fitted.values, data_train$expenses)*100## [1] 42.95781The value of MAPE from the testing data is 42.95781, then the model is categorized as reasonable foresting.
Output Prediction
head(data_test)## age sex bmi children smoker region expenses
## 5 32 male 28.9 0 no northwest 3866.86
## 7 46 female 33.4 1 no southeast 8240.59
## 11 25 male 26.2 0 no northeast 2721.32
## 13 23 male 34.4 0 no southwest 1826.84
## 14 56 female 39.8 0 no southeast 11090.72
## 29 23 male 17.4 1 no northwest 2775.19library(DT)
datatable(data_test)Model Assumption
Linearity
resfit <- data.frame(residual = model_backward$residuals, fitted = model_backward$fitted.values)
resfit %>% ggplot(aes(fitted, residual)) +
geom_point() +
geom_hline(aes(yintercept = 0, colour= "red")) +
theme_minimal() +
labs(title = "Residual vs Fitted Plot") +
theme(legend.position = "none") 
The result:
Based on the patterns obtained, there is little to no linearity in the model.
Normality of Residual
par(mfrow=c(1,2))
hist(model_all$residuals, breaks = 10)
qqnorm(residuals(model_backward), main="Normal Q-Q Plot")
qqline(residuals(model_backward)) 
# Shapiro Test
shapiro.test(model_backward$residuals)##
## Shapiro-Wilk normality test
##
## data: model_backward$residuals
## W = 0.8975, p-value < 2.2e-16The result:
Based on the test results, the p-value obtained is <0.05 so the model does NOT fulfill the assumption of normality.
No Multucolinearity
vif(model_backward)## GVIF Df GVIF^(1/(2*Df))
## age 1.011690 1 1.005828
## bmi 1.108901 1 1.053044
## children 1.004036 1 1.002016
## smoker 1.007921 1 1.003953
## region 1.109869 3 1.017525The result: Based on the VIF value, VIF < 10, meaning there is no multicollinearity.
No Autocorrelation
durbinWatsonTest(model_backward)## lag Autocorrelation D-W Statistic p-value
## 1 0.04038038 1.915413 0.162
## Alternative hypothesis: rho != 0The result: The result of autocorrelation shows that the null hypothesis is not rejected, meaning that the residual model has no autocorrelation in it.
Homoscedasticity of Residual
plot(x = model_backward$fitted.values, y = model_backward$residuals)
abline(h = 0, col = "red", lty = 2)
library(lmtest)## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
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## as.Date, as.Date.numericbptest(model_backward)##
## studentized Breusch-Pagan test
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## data: model_backward
## BP = 102.47, df = 7, p-value < 2.2e-16The result: The null hypothesis is rejected, so the residuals of the model have non-uniform variance (heteroscedasticity).
Other Model (Random Forest)
library(randomForest)## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
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## liftBuilding Model
model_rf <- randomForest(formula = expenses ~ . , data = insurance, ntree = 1000, mtry = 3,
keep.forest = FALSE, importance = TRUE)
model_rf##
## Call:
## randomForest(formula = expenses ~ ., data = insurance, ntree = 1000, mtry = 3, keep.forest = FALSE, importance = TRUE)
## Type of random forest: regression
## Number of trees: 1000
## No. of variables tried at each split: 3
##
## Mean of squared residuals: 21422527
## % Var explained: 85.38Prediction
pred_rf <- model_rf$predicted
insurance_rf <- insurance
insurance_rf$prediction <- pred_rf
insurance_rf## age sex bmi children smoker region expenses prediction
## 1 19 female 27.9 0 yes southwest 16884.92 17523.982
## 2 18 male 33.8 1 no southeast 1725.55 4314.529
## 3 28 male 33.0 3 no southeast 4449.46 5359.635
## 4 33 male 22.7 0 no northwest 21984.47 4776.347
## 5 32 male 28.9 0 no northwest 3866.86 4790.374
## 6 31 female 25.7 0 no southeast 3756.62 4282.499
## 7 46 female 33.4 1 no southeast 8240.59 8951.806
## 8 37 female 27.7 3 no northwest 7281.51 7220.544
## 9 37 male 29.8 2 no northeast 6406.41 9479.815
## 10 60 female 25.8 0 no northwest 28923.14 13126.767
## 11 25 male 26.2 0 no northeast 2721.32 3015.428
## 12 62 female 26.3 0 yes southeast 27808.73 28564.598
## 13 23 male 34.4 0 no southwest 1826.84 3910.943
## 14 56 female 39.8 0 no southeast 11090.72 11780.507
## 15 27 male 42.1 0 yes southeast 39611.76 38955.760
## 16 19 male 24.6 1 no southwest 1837.24 2426.981
## 17 52 female 30.8 1 no northeast 10797.34 14572.398
## 18 23 male 23.8 0 no northeast 2395.17 4098.735
## 19 56 male 40.3 0 no southwest 10602.39 12217.337
## 20 30 male 35.3 0 yes southwest 36837.47 38013.128
## 21 60 female 36.0 0 no northeast 13228.85 14435.393
## 22 30 female 32.4 1 no southwest 4149.74 6165.240
## 23 18 male 34.1 0 no southeast 1137.01 2374.700
## 24 34 female 31.9 1 yes northeast 37701.88 39412.515
## 25 37 male 28.0 2 no northwest 6203.90 6747.892
## 26 59 female 27.7 3 no southeast 14001.13 15425.142
## 27 63 female 23.1 0 no northeast 14451.84 13966.924
## 28 55 female 32.8 2 no northwest 12268.63 13931.922
## 29 23 male 17.4 1 no northwest 2775.19 8199.519
## 30 31 male 36.3 2 yes southwest 38711.00 42967.703
## 31 22 male 35.6 0 yes southwest 35585.58 36340.038
## 32 18 female 26.3 0 no northeast 2198.19 3162.976
## 33 19 female 28.6 5 no southwest 4687.80 8068.351
## 34 63 male 28.3 0 no northwest 13770.10 15324.431
## 35 28 male 36.4 1 yes southwest 51194.56 38404.998
## 36 19 male 20.4 0 no northwest 1625.43 1968.371
## 37 62 female 33.0 3 no northwest 15612.19 16690.055
## 38 26 male 20.8 0 no southwest 2302.30 3216.133
## 39 35 male 36.7 1 yes northeast 39774.28 42968.473
## 40 60 male 39.9 0 yes southwest 48173.36 47997.086
## 41 24 female 26.6 0 no northeast 3046.06 4870.983
## 42 31 female 36.6 2 no southeast 4949.76 5369.328
## 43 41 male 21.8 1 no southeast 6272.48 8088.506
## 44 37 female 30.8 2 no southeast 6313.76 7194.058
## 45 38 male 37.1 1 no northeast 6079.67 8017.820
## 46 55 male 37.3 0 no southwest 20630.28 11327.324
## 47 18 female 38.7 2 no northeast 3393.36 8795.417
## 48 28 female 34.8 0 no northwest 3556.92 6134.524
## 49 60 female 24.5 0 no southeast 12629.90 16965.521
## 50 36 male 35.2 1 yes southeast 38709.18 39060.788
## 51 18 female 35.6 0 no northeast 2211.13 3727.519
## 52 21 female 33.6 2 no northwest 3579.83 14460.670
## 53 48 male 28.0 1 yes southwest 23568.27 26843.376
## 54 36 male 34.4 0 yes southeast 37742.58 38012.368
## 55 40 female 28.7 3 no northwest 8059.68 7826.172
## 56 58 male 37.0 2 yes northwest 47496.49 46789.628
## 57 58 female 31.8 2 no northeast 13607.37 15117.838
## 58 18 male 31.7 2 yes southeast 34303.17 34584.405
## 59 53 female 22.9 1 yes southeast 23244.79 24023.070
## 60 34 female 37.3 2 no northwest 5989.52 6968.595
## 61 43 male 27.4 3 no northeast 8606.22 11126.860
## 62 25 male 33.7 4 no southeast 4504.66 7327.829
## 63 64 male 24.7 1 no northwest 30166.62 14414.655
## 64 28 female 25.9 1 no northwest 4133.64 5423.613
## 65 20 female 22.4 0 yes northwest 14711.74 18372.482
## 66 19 female 28.9 0 no southwest 1743.21 2191.316
## 67 61 female 39.1 2 no southwest 14235.07 18724.134
## 68 40 male 26.3 1 no northwest 6389.38 6550.319
## 69 40 female 36.2 0 no southeast 5920.10 7466.714
## 70 28 male 24.0 3 yes southeast 17663.14 18766.472
## 71 27 female 24.8 0 yes southeast 16577.78 19835.968
## 72 31 male 28.5 5 no northeast 6799.46 9168.311
## 73 53 female 28.1 3 no southwest 11741.73 12409.705
## 74 58 male 32.0 1 no southeast 11946.63 13408.288
## 75 44 male 27.4 2 no southwest 7726.85 9767.729
## 76 57 male 34.0 0 no northwest 11356.66 11659.792
## 77 29 female 29.6 1 no southeast 3947.41 5341.448
## 78 21 male 35.5 0 no southeast 1532.47 2814.396
## 79 22 female 39.8 0 no northeast 2755.02 3426.399
## 80 41 female 33.0 0 no northwest 6571.02 6747.968
## 81 31 male 26.9 1 no northeast 4441.21 5148.293
## 82 45 female 38.3 0 no northeast 7935.29 10976.443
## 83 22 male 37.6 1 yes southeast 37165.16 38263.881
## 84 48 female 41.2 4 no northwest 11033.66 12898.807
## 85 37 female 34.8 2 yes southwest 39836.52 40161.299
## 86 45 male 22.9 2 yes northwest 21098.55 25753.714
## 87 57 female 31.2 0 yes northwest 43578.94 42793.974
## 88 56 female 27.2 0 no southwest 11073.18 12722.312
## 89 46 female 27.7 0 no northwest 8026.67 12533.390
## 90 55 female 27.0 0 no northwest 11082.58 14045.216
## 91 21 female 39.5 0 no southeast 2026.97 2625.253
## 92 53 female 24.8 1 no northwest 10942.13 14003.730
## 93 59 male 29.8 3 yes northeast 30184.94 29752.375
## 94 35 male 34.8 2 no northwest 5729.01 7607.593
## 95 64 female 31.3 2 yes southwest 47291.06 45800.615
## 96 28 female 37.6 1 no southeast 3766.88 5097.883
## 97 54 female 30.8 3 no southwest 12105.32 12515.302
## 98 55 male 38.3 0 no southeast 10226.28 13404.682
## 99 56 male 20.0 0 yes northeast 22412.65 25218.475
## 100 38 male 19.3 0 yes southwest 15820.70 18819.549
## 101 41 female 31.6 0 no southwest 6186.13 6365.426
## 102 30 male 25.5 0 no northeast 3645.09 3856.083
## 103 18 female 30.1 0 no northeast 21344.85 2778.935
## 104 61 female 29.9 3 yes southeast 30942.19 30700.357
## 105 34 female 27.5 1 no southwest 5003.85 6432.813
## 106 20 male 28.0 1 yes northwest 17560.38 18289.501
## 107 19 female 28.4 1 no southwest 2331.52 2987.527
## 108 26 male 30.9 2 no northwest 3877.30 6701.758
## 109 29 male 27.9 0 no southeast 2867.12 3721.297
## 110 63 male 35.1 0 yes southeast 47055.53 46655.564
## 111 54 male 33.6 1 no northwest 10825.25 12021.459
## 112 55 female 29.7 2 no southwest 11881.36 13533.117
## 113 37 male 30.8 0 no southwest 4646.76 10088.731
## 114 21 female 35.7 0 no northwest 2404.73 2434.672
## 115 52 male 32.2 3 no northeast 11488.32 16166.305
## 116 60 male 28.6 0 no northeast 30260.00 12752.131
## 117 58 male 49.1 0 no southeast 11381.33 14866.801
## 118 29 female 27.9 1 yes southeast 19107.78 19035.365
## 119 49 female 27.2 0 no southeast 8601.33 12514.242
## 120 37 female 23.4 2 no northwest 6686.43 7531.048
## 121 44 male 37.1 2 no southwest 7740.34 9255.123
## 122 18 male 23.8 0 no northeast 1705.62 2016.430
## 123 20 female 29.0 0 no northwest 2257.48 2503.066
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## 1306 24 female 27.7 0 no southeast 2464.62 7537.604
## 1307 29 female 21.9 0 yes northeast 16115.30 16783.911
## 1308 32 male 28.1 4 yes northwest 21472.48 21592.081
## 1309 25 female 30.2 0 yes southwest 33900.65 33443.305
## 1310 41 male 32.2 2 no southwest 6875.96 7429.582
## 1311 42 male 26.3 1 no northwest 6940.91 6912.273
## 1312 33 female 26.7 0 no northwest 4571.41 6114.510
## 1313 34 male 42.9 1 no southwest 4536.26 8487.426
## 1314 19 female 34.7 2 yes southwest 36397.58 35850.468
## 1315 30 female 23.7 3 yes northwest 18765.88 19207.945
## 1316 18 male 28.3 1 no northeast 11272.33 3016.696
## 1317 19 female 20.6 0 no southwest 1731.68 1877.350
## 1318 18 male 53.1 0 no southeast 1163.46 3364.567
## 1319 35 male 39.7 4 no northeast 19496.72 11705.414
## 1320 39 female 26.3 2 no northwest 7201.70 7239.638
## 1321 31 male 31.1 3 no northwest 5425.02 6094.678
## 1322 62 male 26.7 0 yes northeast 28101.33 28348.814
## 1323 62 male 38.8 0 no southeast 12981.35 13288.216
## 1324 42 female 40.4 2 yes southeast 43896.38 42448.473
## 1325 31 male 25.9 1 no northwest 4239.89 4673.302
## 1326 61 male 33.5 0 no northeast 13143.34 13717.046
## 1327 42 female 32.9 0 no northeast 7050.02 7294.635
## 1328 51 male 30.0 1 no southeast 9377.90 10106.569
## 1329 23 female 24.2 2 no northeast 22395.74 9276.102
## 1330 52 male 38.6 2 no southwest 10325.21 12671.787
## 1331 57 female 25.7 2 no southeast 12629.17 15754.181
## 1332 23 female 33.4 0 no southwest 10795.94 5061.915
## 1333 52 female 44.7 3 no southwest 11411.69 12763.734
## 1334 50 male 31.0 3 no northwest 10600.55 13272.114
## 1335 18 female 31.9 0 no northeast 2205.98 4161.189
## 1336 18 female 36.9 0 no southeast 1629.83 3692.866
## 1337 21 female 25.8 0 no southwest 2007.95 2337.389
## 1338 61 female 29.1 0 yes northwest 29141.36 28671.121Prediction Result
datatable(insurance_rf)Variable Important
importance <- as.data.frame(varImp(model_rf))
importance %>%
arrange(-Overall)## Overall
## smoker 317.089953
## bmi 160.533832
## age 156.698809
## children 28.766011
## region 11.925082
## sex -3.878593